Group Project 4 COVID
covid_p11
2022-03-28
Topic: COVID-19 New cases visualisation dashboard: Shiny app
covid = read.csv("owid-covid-data.csv")
# these are indices for each country (an index is just a collection of stocks)
# 'FileEncoding' just cleans the column encoding for this case
#sp500 = read.csv("SPY Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for US
#TOPIX = read.csv("TOPIX Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for Japan
ASX200 = read.csv("S&P_ASX 200 Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for Australia
#NSEI = read.csv("Nifty 50 Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for India
#SSEC = read.csv("Shanghai Composite Historical Data.csv", fileEncoding = 'UTF-8-BOM') # this is for China
colnames(covid)## [1] "iso_code"
## [2] "continent"
## [3] "location"
## [4] "date"
## [5] "total_cases"
## [6] "new_cases"
## [7] "new_cases_smoothed"
## [8] "total_deaths"
## [9] "new_deaths"
## [10] "new_deaths_smoothed"
## [11] "total_cases_per_million"
## [12] "new_cases_per_million"
## [13] "new_cases_smoothed_per_million"
## [14] "total_deaths_per_million"
## [15] "new_deaths_per_million"
## [16] "new_deaths_smoothed_per_million"
## [17] "reproduction_rate"
## [18] "icu_patients"
## [19] "icu_patients_per_million"
## [20] "hosp_patients"
## [21] "hosp_patients_per_million"
## [22] "weekly_icu_admissions"
## [23] "weekly_icu_admissions_per_million"
## [24] "weekly_hosp_admissions"
## [25] "weekly_hosp_admissions_per_million"
## [26] "total_tests"
## [27] "new_tests"
## [28] "total_tests_per_thousand"
## [29] "new_tests_per_thousand"
## [30] "new_tests_smoothed"
## [31] "new_tests_smoothed_per_thousand"
## [32] "positive_rate"
## [33] "tests_per_case"
## [34] "tests_units"
## [35] "total_vaccinations"
## [36] "people_vaccinated"
## [37] "people_fully_vaccinated"
## [38] "total_boosters"
## [39] "new_vaccinations"
## [40] "new_vaccinations_smoothed"
## [41] "total_vaccinations_per_hundred"
## [42] "people_vaccinated_per_hundred"
## [43] "people_fully_vaccinated_per_hundred"
## [44] "total_boosters_per_hundred"
## [45] "new_vaccinations_smoothed_per_million"
## [46] "new_people_vaccinated_smoothed"
## [47] "new_people_vaccinated_smoothed_per_hundred"
## [48] "stringency_index"
## [49] "population"
## [50] "population_density"
## [51] "median_age"
## [52] "aged_65_older"
## [53] "aged_70_older"
## [54] "gdp_per_capita"
## [55] "extreme_poverty"
## [56] "cardiovasc_death_rate"
## [57] "diabetes_prevalence"
## [58] "female_smokers"
## [59] "male_smokers"
## [60] "handwashing_facilities"
## [61] "hospital_beds_per_thousand"
## [62] "life_expectancy"
## [63] "human_development_index"
## [64] "excess_mortality_cumulative_absolute"
## [65] "excess_mortality_cumulative"
## [66] "excess_mortality"
## [67] "excess_mortality_cumulative_per_million"data clean
#Dropping NA
covid_clean = covid %>% drop_na(new_cases, new_cases_smoothed, new_vaccinations, new_vaccinations_smoothed, new_vaccinations_smoothed_per_million, population, population_density, median_age, extreme_poverty, total_vaccinations, hospital_beds_per_thousand, human_development_index, new_deaths, new_tests, weekly_icu_admissions_per_million, weekly_icu_admissions)
#This code changes the negative case values in the data set to zero
covid_clean$new_cases[covid_clean$new_cases < 0] <- 0
covid_AUS <- covid %>% filter(location == "Australia")
covid_clean_AUS = covid_AUS %>% drop_na(new_cases, new_cases_smoothed, new_vaccinations, new_vaccinations_smoothed, new_vaccinations_smoothed_per_million, population, population_density, median_age, extreme_poverty, total_vaccinations, hospital_beds_per_thousand, human_development_index, new_deaths, new_tests)
#This code changes the negative case values in the data set to zero
covid_clean_AUS$new_cases[covid_clean_AUS$new_cases < 0] <- 0
view(covid_clean_AUS)
#glimpse(covid_clean)
#covid_clean$date = as.Date(covid_clean$date)
#max(covid_clean$date)
#unique(covid_clean$location)Question 1
#code for more optimised join (to be pasted later)
class(covid_clean$date)## [1] "character"performing join
# using a copy just in case
covid2 <- covid_clean_AUS
#sp500$Date = mdy(sp500$Date)
#TOPIX$Date = mdy(TOPIX$Date)
ASX200$date = mdy(ASX200$Date)
#NSEI$Date = mdy(NSEI$Date)
#SSEC$Date = mdy(SSEC$Date)
#sp500$date = mdy(sp500$Date)
#TOPIX$date = mdy(TOPIX$Date)
ASX200$date = mdy(ASX200$Date)
#NSEI$date = mdy(NSEI$Date)
#SSEC$date = mdy(SSEC$Date)
covid2$date = ymd(covid2$date)
#Temporarily changing the date to character as joining cannot be done with date objects
#Also selecting relevant columns for analysis later
covid2 <- covid2 %>%
transform(covid2, date = as.character(date)) %>%
select(date, new_cases, new_deaths, location, new_vaccinations, new_tests, population, population_density)
#sp500$Date <- as.character(sp500$Date)
#TOPIX$Date <- as.character(TOPIX$Date)
ASX200$date <- as.character(ASX200$date)
#NSEI$Date <- as.character(NSEI$Date)
#SSEC$Date <- as.character(SSEC$Date)
#sp500$date <- as.character(sp500$Date)
#TOPIX$date <- as.character(TOPIX$Date)
ASX200$date <- as.character(ASX200$date)
#NSEI$date <- as.character(NSEI$Date)
#SSEC$date <- as.character(SSEC$Date)
# renaming column so it has same name as the stock market data frames for joining later
#colnames(covid2)[1] = "Date"
# making data frames for each country we select to perform individual joins on each to their respective stock market index
#covid_US <- covid2 %>% filter(location == "United States")
covid_AUS <- covid2 %>% filter(location == "Australia")
#covid_IND <- covid2 %>% filter(location == "India")
#covid_JPN <- covid2 %>% filter(location == "Japan")
#covid_CHN <- covid2 %>% filter(location == "China")
# performing joins
#df_1 = inner_join(sp500, covid_US, by = "Date")
#df_2 = inner_join(TOPIX, covid_JPN, by = "Date")
df_3 = inner_join(ASX200, covid_AUS, by = "date")
#df_4 = inner_join(NSEI, covid_IND, by = "Date")
#df_5 = inner_join(SSEC, covid_CHN, by = "Date")
#df_1 = inner_join(sp500, covid_US, by = "date")
#df_2 = inner_join(TOPIX, covid_JPN, by = "date")
df_3 = inner_join(ASX200, covid_AUS, by = "date")
#df_4 = inner_join(NSEI, covid_IND, by = "date")
#df_5 = inner_join(SSEC, covid_CHN, by = "date")
# vertically joined data set (now one column will store all the values of the respective country index)
# e.g US stores prices relevant to S&P500 and China's prices are relevant to the the SSEC which is based in Shanghai.
#covid_joined <- rbind(df_1, df_2, df_3, df_4, df_5)
# Still need transform relevant column to numeric ect...will do a little later
df_aus <- df_3
#transformation
df_aus$date = as.Date(df_aus$date)
df_aus$Price = as.numeric(gsub(",","",df_aus$Price))
df_aus## Date Price Open High Low Vol. Change.. date
## 1 Mar 25, 2022 7406.2 7,387.10 7,431.30 7,387.10 679.65M 0.26% 2022-03-25
## 2 Mar 24, 2022 7387.1 7,377.90 7,399.40 7,356.20 633.60M 0.12% 2022-03-24
## 3 Mar 23, 2022 7377.9 7,341.10 7,386.90 7,329.80 575.42M 0.50% 2022-03-23
## 4 Mar 22, 2022 7341.1 7,278.50 7,377.30 7,278.50 634.67M 0.86% 2022-03-22
## 5 Mar 21, 2022 7278.5 7,294.40 7,350.40 7,278.50 561.98M -0.22% 2022-03-21
## 6 Mar 18, 2022 7294.4 7,250.80 7,294.40 7,245.90 1.54B 0.60% 2022-03-18
## 7 Mar 17, 2022 7250.8 7,175.20 7,296.80 7,175.20 831.38M 1.05% 2022-03-17
## 8 Mar 16, 2022 7175.2 7,097.40 7,180.20 7,097.40 701.73M 1.10% 2022-03-16
## 9 Mar 15, 2022 7097.4 7,149.40 7,149.40 7,080.70 788.37M -0.73% 2022-03-15
## 10 Mar 14, 2022 7149.4 7,063.60 7,149.40 7,063.60 599.30M 1.21% 2022-03-14
## 11 Mar 11, 2022 7063.6 7,130.80 7,151.40 7,051.20 816.85M -0.94% 2022-03-11
## 12 Mar 10, 2022 7130.8 7,053.00 7,161.00 7,039.70 976.57M 1.10% 2022-03-10
## 13 Mar 09, 2022 7053.0 6,980.30 7,072.00 6,968.90 1.04B 1.04% 2022-03-09
## 14 Mar 08, 2022 6980.3 7,038.60 7,053.50 6,980.30 973.85M -0.83% 2022-03-08
## 15 Mar 07, 2022 7038.6 7,110.80 7,133.70 7,010.50 1.02B -1.02% 2022-03-07
## 16 Mar 04, 2022 7110.8 7,151.40 7,151.40 7,025.20 972.29M -0.57% 2022-03-04
## 17 Mar 03, 2022 7151.4 7,116.70 7,198.10 7,116.70 856.24M 0.49% 2022-03-03
## 18 Mar 02, 2022 7116.7 7,096.50 7,121.50 7,041.90 941.03M 0.28% 2022-03-02
## 19 Mar 01, 2022 7096.5 7,049.10 7,159.90 7,049.10 812.87M 0.67% 2022-03-01
## 20 Feb 28, 2022 7049.1 6,997.80 7,049.60 6,979.40 1.01B 0.73% 2022-02-28
## 21 Feb 25, 2022 6997.8 6,990.60 7,045.60 6,974.90 888.81M 0.10% 2022-02-25
## 22 Feb 24, 2022 6990.6 7,205.70 7,205.70 6,959.30 1.11B -2.99% 2022-02-24
## 23 Feb 23, 2022 7205.7 7,161.30 7,205.70 7,145.40 788.38M 0.62% 2022-02-23
## 24 Feb 15, 2022 7206.9 7,243.90 7,251.40 7,198.00 730.13M -0.51% 2022-02-15
## 25 Feb 14, 2022 7243.9 7,217.30 7,263.60 7,181.70 749.35M 0.37% 2022-02-14
## 26 Feb 11, 2022 7217.3 7,288.50 7,294.40 7,191.80 629.39M -0.98% 2022-02-11
## 27 Feb 10, 2022 7288.5 7,268.30 7,336.60 7,260.80 886.64M 0.28% 2022-02-10
## 28 Feb 09, 2022 7268.3 7,186.70 7,268.30 7,183.00 978.75M 1.14% 2022-02-09
## 29 Feb 08, 2022 7186.7 7,110.80 7,203.00 7,110.80 771.03M 1.07% 2022-02-08
## 30 Feb 07, 2022 7110.8 7,120.20 7,128.30 7,046.50 617.94M -0.13% 2022-02-07
## 31 Jan 28, 2022 6988.1 6,838.30 7,000.00 6,837.90 2.09B 2.19% 2022-01-28
## 32 Jan 27, 2022 6838.3 6,961.60 7,042.80 6,758.20 1.18B -1.77% 2022-01-27
## 33 Jan 25, 2022 6961.6 7,139.50 7,139.50 6,920.70 1.10B -2.49% 2022-01-25
## 34 Jan 24, 2022 7139.5 7,173.70 7,173.70 7,086.80 760.46M -0.51% 2022-01-24
## 35 Jan 21, 2022 7175.8 7,342.40 7,342.40 7,153.30 910.99M -2.27% 2022-01-21
## 36 Jan 20, 2022 7342.4 7,332.50 7,354.60 7,298.50 684.95M 0.14% 2022-01-20
## 37 Jan 19, 2022 7332.5 7,408.80 7,408.80 7,325.70 697.49M -1.03% 2022-01-19
## 38 Jan 18, 2022 7408.8 7,417.30 7,445.10 7,398.20 549.27M -0.11% 2022-01-18
## 39 Jan 17, 2022 7417.3 7,393.90 7,429.30 7,385.80 472.82M 0.32% 2022-01-17
## 40 Jan 14, 2022 7393.9 7,474.40 7,474.40 7,386.80 619.98M -1.08% 2022-01-14
## 41 Jan 13, 2022 7474.4 7,438.90 7,487.30 7,438.90 605.24M 0.48% 2022-01-13
## 42 Jan 12, 2022 7438.9 7,390.10 7,467.50 7,390.10 569.40M 0.66% 2022-01-12
## 43 Jan 11, 2022 7390.1 7,447.10 7,447.10 7,376.80 549.65M -0.77% 2022-01-11
## 44 Jan 10, 2022 7447.1 7,453.30 7,460.20 7,409.90 410.07M -0.08% 2022-01-10
## 45 Jan 07, 2022 7453.3 7,358.30 7,484.60 7,358.30 469.08M 1.29% 2022-01-07
## 46 Jan 06, 2022 7358.3 7,565.80 7,565.80 7,340.40 623.92M -2.74% 2022-01-06
## 47 Jan 05, 2022 7565.8 7,589.80 7,620.20 7,563.40 472.27M -0.32% 2022-01-05
## 48 Jan 04, 2022 7589.8 7,444.60 7,594.70 7,444.60 503.96M 1.95% 2022-01-04
## 49 Dec 31, 2021 7444.6 7,513.40 7,515.30 7,444.60 272.41M -0.92% 2021-12-31
## 50 Dec 30, 2021 7513.4 7,509.80 7,520.60 7,499.50 365.35M 0.05% 2021-12-30
## 51 Dec 29, 2021 7509.8 7,420.30 7,518.40 7,420.30 428.61M 1.21% 2021-12-29
## 52 Dec 24, 2021 7420.3 7,387.60 7,435.90 7,387.60 230.00M 0.44% 2021-12-24
## 53 Dec 23, 2021 7387.6 7,364.80 7,404.20 7,363.60 390.03M 0.31% 2021-12-23
## 54 Dec 22, 2021 7364.8 7,355.00 7,374.50 7,333.00 539.85M 0.13% 2021-12-22
## 55 Dec 21, 2021 7355.0 7,292.20 7,358.30 7,287.30 571.41M 0.86% 2021-12-21
## 56 Dec 20, 2021 7292.2 7,304.00 7,304.00 7,257.80 543.60M -0.16% 2021-12-20
## 57 Dec 17, 2021 7304.0 7,295.70 7,350.30 7,295.70 1.22B 0.11% 2021-12-17
## 58 Dec 16, 2021 7295.7 7,327.10 7,334.50 7,277.10 787.43M -0.43% 2021-12-16
## 59 Dec 15, 2021 7327.1 7,378.40 7,378.40 7,325.70 514.72M -0.70% 2021-12-15
## 60 Dec 14, 2021 7378.4 7,379.30 7,393.50 7,341.30 581.39M -0.01% 2021-12-14
## 61 Dec 13, 2021 7379.3 7,353.50 7,417.70 7,353.50 452.15M 0.35% 2021-12-13
## 62 Dec 10, 2021 7353.5 7,384.50 7,384.50 7,336.70 599.53M -0.42% 2021-12-10
## 63 Dec 09, 2021 7384.5 7,405.40 7,418.30 7,377.70 542.21M -0.28% 2021-12-09
## 64 Dec 08, 2021 7405.4 7,313.90 7,443.40 7,313.90 675.57M 1.25% 2021-12-08
## 65 Dec 07, 2021 7313.9 7,245.10 7,326.10 7,245.10 601.67M 0.95% 2021-12-07
## 66 Dec 06, 2021 7245.1 7,241.20 7,257.90 7,207.80 650.43M 0.05% 2021-12-06
## 67 Dec 03, 2021 7241.2 7,225.20 7,288.20 7,211.50 631.36M 0.22% 2021-12-03
## 68 Dec 02, 2021 7225.2 7,235.90 7,239.70 7,168.90 652.44M -0.15% 2021-12-02
## 69 Dec 01, 2021 7235.9 7,256.00 7,262.30 7,183.40 654.05M -0.28% 2021-12-01
## 70 Nov 30, 2021 7256.0 7,239.80 7,332.60 7,239.80 1.12B 0.22% 2021-11-30
## 71 Nov 29, 2021 7239.8 7,279.30 7,279.30 7,180.30 817.95M -0.54% 2021-11-29
## 72 Nov 26, 2021 7279.3 7,407.30 7,407.30 7,260.30 577.17M -1.73% 2021-11-26
## 73 Nov 25, 2021 7407.3 7,399.40 7,412.70 7,373.70 597.77M 0.11% 2021-11-25
## 74 Nov 24, 2021 7399.4 7,410.60 7,425.00 7,381.80 595.33M -0.15% 2021-11-24
## 75 Nov 23, 2021 7410.6 7,353.10 7,416.60 7,353.10 606.30M 0.78% 2021-11-23
## 76 Nov 22, 2021 7353.1 7,396.50 7,396.50 7,337.00 486.26M -0.59% 2021-11-22
## 77 Nov 19, 2021 7396.5 7,379.20 7,404.90 7,376.00 525.25M 0.23% 2021-11-19
## 78 Nov 18, 2021 7379.2 7,369.90 7,398.20 7,343.80 634.51M 0.13% 2021-11-18
## 79 Nov 17, 2021 7369.9 7,420.40 7,431.10 7,342.90 626.09M -0.68% 2021-11-17
## 80 Nov 16, 2021 7420.4 7,470.10 7,470.10 7,403.50 626.13M -0.67% 2021-11-16
## 81 Nov 15, 2021 7470.1 7,443.00 7,479.10 7,437.90 499.12M 0.36% 2021-11-15
## 82 Nov 12, 2021 7443.0 7,381.90 7,465.80 7,381.90 572.67M 0.83% 2021-11-12
## 83 Nov 11, 2021 7381.9 7,423.90 7,423.90 7,329.50 601.08M -0.57% 2021-11-11
## 84 Nov 10, 2021 7423.9 7,434.20 7,460.50 7,411.90 549.06M -0.14% 2021-11-10
## 85 Nov 09, 2021 7434.2 7,452.20 7,468.60 7,434.20 609.74M -0.24% 2021-11-09
## 86 Nov 08, 2021 7452.2 7,456.90 7,474.30 7,433.40 581.05M -0.06% 2021-11-08
## 87 Nov 05, 2021 7456.9 7,428.00 7,477.20 7,424.40 568.02M 0.39% 2021-11-05
## 88 Nov 04, 2021 7428.0 7,392.70 7,428.00 7,392.70 658.11M 0.48% 2021-11-04
## 89 Nov 03, 2021 7392.7 7,324.30 7,431.60 7,324.30 587.77M 0.93% 2021-11-03
## 90 Nov 02, 2021 7324.3 7,370.80 7,395.90 7,311.60 491.50M -0.63% 2021-11-02
## 91 Nov 01, 2021 7370.8 7,323.70 7,388.30 7,323.70 612.05M 0.64% 2021-11-01
## 92 Oct 29, 2021 7323.7 7,430.40 7,447.00 7,318.80 695.89M -1.44% 2021-10-29
## 93 Oct 28, 2021 7430.4 7,448.70 7,448.70 7,404.60 578.21M -0.25% 2021-10-28
## 94 Oct 27, 2021 7448.7 7,443.40 7,473.90 7,419.70 596.69M 0.07% 2021-10-27
## 95 Oct 26, 2021 7443.4 7,441.00 7,471.30 7,439.20 517.77M 0.03% 2021-10-26
## 96 Oct 25, 2021 7441.0 7,415.50 7,471.70 7,415.50 464.11M 0.34% 2021-10-25
## 97 Oct 22, 2021 7415.5 7,415.40 7,431.40 7,391.30 638.59M 0.00% 2021-10-22
## 98 Oct 21, 2021 7415.4 7,413.70 7,446.70 7,403.70 751.88M 0.02% 2021-10-21
## 99 Oct 20, 2021 7413.7 7,374.90 7,449.50 7,374.90 746.76M 0.53% 2021-10-20
## 100 Oct 19, 2021 7374.9 7,381.10 7,406.90 7,373.80 581.73M -0.08% 2021-10-19
## 101 Oct 18, 2021 7381.1 7,362.00 7,393.80 7,354.10 562.54M 0.26% 2021-10-18
## 102 Oct 15, 2021 7362.0 7,311.70 7,373.20 7,311.70 565.80M 0.69% 2021-10-15
## 103 Oct 14, 2021 7311.7 7,272.50 7,358.60 7,272.50 648.07M 0.54% 2021-10-14
## 104 Oct 13, 2021 7272.5 7,280.70 7,294.00 7,256.30 589.78M -0.11% 2021-10-13
## 105 Oct 12, 2021 7280.7 7,299.80 7,330.40 7,260.40 669.14M -0.26% 2021-10-12
## 106 Oct 11, 2021 7299.8 7,320.10 7,320.10 7,250.00 613.01M -0.28% 2021-10-11
## 107 Oct 08, 2021 7320.1 7,256.70 7,322.00 7,256.70 630.30M 0.87% 2021-10-08
## 108 Oct 07, 2021 7256.7 7,206.50 7,265.20 7,206.50 718.86M 0.70% 2021-10-07
## 109 Oct 06, 2021 7206.5 7,248.40 7,279.40 7,183.50 726.94M -0.58% 2021-10-06
## 110 Oct 05, 2021 7248.4 7,278.50 7,278.50 7,202.70 735.94M -0.41% 2021-10-05
## 111 Oct 04, 2021 7278.5 7,185.50 7,305.90 7,185.50 464.15M 1.29% 2021-10-04
## 112 Oct 01, 2021 7185.5 7,332.20 7,332.20 7,157.90 785.10M -2.00% 2021-10-01
## 113 Sep 30, 2021 7332.2 7,196.70 7,332.20 7,196.70 801.81M 1.88% 2021-09-30
## 114 Sep 29, 2021 7196.7 7,275.60 7,275.60 7,145.70 850.92M -1.08% 2021-09-29
## 115 Sep 28, 2021 7275.6 7,384.20 7,384.20 7,275.60 753.73M -1.47% 2021-09-28
## 116 Sep 27, 2021 7384.2 7,342.60 7,416.40 7,342.60 630.60M 0.57% 2021-09-27
## 117 Sep 24, 2021 7342.6 7,370.20 7,377.00 7,334.20 666.26M -0.37% 2021-09-24
## 118 Sep 23, 2021 7370.2 7,296.90 7,387.00 7,296.90 696.69M 1.00% 2021-09-23
## 119 Sep 22, 2021 7296.9 7,273.80 7,338.60 7,241.80 663.95M 0.32% 2021-09-22
## 120 Sep 21, 2021 7273.8 7,248.20 7,285.90 7,191.70 740.27M 0.35% 2021-09-21
## 121 Sep 20, 2021 7248.2 7,390.40 7,390.40 7,233.60 721.06M -2.10% 2021-09-20
## 122 Sep 17, 2021 7403.7 7,460.20 7,460.20 7,376.10 1.61B -0.76% 2021-09-17
## 123 Sep 16, 2021 7460.2 7,417.00 7,487.60 7,417.00 908.93M 0.58% 2021-09-16
## 124 Sep 15, 2021 7417.0 7,437.30 7,437.30 7,378.90 783.02M -0.27% 2021-09-15
## 125 Sep 14, 2021 7437.3 7,425.20 7,446.00 7,388.90 810.67M 0.16% 2021-09-14
## 126 Sep 13, 2021 7425.2 7,406.60 7,436.80 7,389.40 591.65M 0.25% 2021-09-13
## 127 Sep 10, 2021 7406.6 7,369.50 7,430.00 7,369.50 599.12M 0.50% 2021-09-10
## 128 Sep 09, 2021 7369.5 7,512.00 7,512.00 7,344.30 740.25M -1.90% 2021-09-09
## 129 Sep 08, 2021 7512.0 7,530.30 7,530.30 7,476.30 688.88M -0.24% 2021-09-08
## 130 Sep 07, 2021 7530.3 7,528.50 7,536.90 7,487.80 591.48M 0.02% 2021-09-07
## 131 Sep 06, 2021 7528.5 7,522.90 7,528.50 7,440.10 807.64M 0.07% 2021-09-06
## 132 Sep 03, 2021 7522.9 7,485.70 7,539.00 7,485.70 586.72M 0.50% 2021-09-03
## 133 Sep 02, 2021 7485.7 7,527.10 7,527.10 7,437.20 631.41M -0.55% 2021-09-02
## 134 Sep 01, 2021 7527.1 7,534.90 7,534.90 7,462.20 618.48M -0.10% 2021-09-01
## 135 Aug 31, 2021 7534.9 7,504.50 7,555.90 7,504.50 806.31M 0.41% 2021-08-31
## 136 Aug 30, 2021 7504.5 7,488.30 7,528.30 7,476.00 648.07M 0.22% 2021-08-30
## 137 Aug 27, 2021 7488.3 7,491.20 7,494.50 7,465.00 756.50M -0.04% 2021-08-27
## 138 Aug 26, 2021 7491.2 7,531.90 7,531.90 7,477.80 771.81M -0.54% 2021-08-26
## 139 Aug 25, 2021 7531.9 7,503.00 7,542.40 7,503.00 710.52M 0.39% 2021-08-25
## 140 Aug 24, 2021 7503.0 7,489.90 7,524.40 7,489.90 685.47M 0.17% 2021-08-24
## 141 Aug 23, 2021 7489.9 7,460.90 7,496.30 7,460.90 622.95M 0.39% 2021-08-23
## 142 Aug 20, 2021 7460.9 7,464.60 7,512.00 7,453.60 712.73M -0.05% 2021-08-20
## 143 Aug 19, 2021 7464.6 7,502.10 7,502.10 7,429.20 747.63M -0.50% 2021-08-19
## 144 Aug 18, 2021 7502.1 7,511.00 7,532.90 7,470.50 653.54M -0.12% 2021-08-18
## 145 Aug 17, 2021 7511.0 7,582.50 7,582.50 7,494.40 559.15M -0.94% 2021-08-17
## 146 Aug 16, 2021 7582.5 7,628.90 7,629.00 7,582.50 647.00M -0.61% 2021-08-16
## 147 Aug 13, 2021 7628.9 7,588.20 7,632.80 7,585.90 605.05M 0.54% 2021-08-13
## 148 Aug 12, 2021 7588.2 7,584.30 7,608.60 7,571.50 627.84M 0.05% 2021-08-12
## 149 Aug 11, 2021 7584.3 7,562.60 7,615.10 7,562.60 643.15M 0.29% 2021-08-11
## 150 Aug 10, 2021 7562.6 7,538.40 7,576.30 7,537.10 525.15M 0.32% 2021-08-10
## 151 Aug 09, 2021 7538.4 7,538.40 7,567.00 7,532.80 526.82M 0.00% 2021-08-09
## 152 Aug 06, 2021 7538.4 7,511.10 7,538.40 7,497.30 539.38M 0.36% 2021-08-06
## 153 Aug 05, 2021 7511.1 7,503.20 7,526.40 7,493.00 510.28M 0.11% 2021-08-05
## 154 Aug 04, 2021 7503.2 7,474.50 7,509.20 7,474.50 499.67M 0.38% 2021-08-04
## 155 Aug 03, 2021 7474.5 7,491.40 7,495.90 7,455.50 623.33M -0.23% 2021-08-03
## 156 Aug 02, 2021 7491.4 7,415.90 7,506.30 7,414.90 576.29M 1.34% 2021-08-02
## 157 Jul 30, 2021 7392.6 7,417.40 7,437.70 7,387.90 632.53M -0.33% 2021-07-30
## 158 Jul 29, 2021 7417.4 7,379.30 7,418.70 7,379.30 514.88M 0.52% 2021-07-29
## 159 Jul 28, 2021 7379.3 7,431.40 7,432.40 7,368.30 585.95M -0.70% 2021-07-28
## 160 Jul 26, 2021 7394.3 7,394.40 7,417.60 7,389.30 548.09M -0.00% 2021-07-26
## 161 Jul 23, 2021 7394.4 7,386.40 7,398.70 7,357.00 493.58M 0.11% 2021-07-23
## 162 Jul 22, 2021 7386.4 7,308.70 7,386.40 7,308.70 582.73M 1.06% 2021-07-22
## 163 Jul 21, 2021 7308.7 7,252.20 7,354.80 7,252.20 581.30M 0.78% 2021-07-21
## 164 Jul 20, 2021 7252.2 7,286.00 7,286.00 7,205.00 644.64M -0.46% 2021-07-20
## 165 Jul 19, 2021 7286.0 7,348.10 7,348.10 7,249.20 496.36M -0.85% 2021-07-19
## 166 Jul 16, 2021 7348.1 7,335.90 7,348.10 7,316.30 592.38M 0.17% 2021-07-16
## 167 Jul 15, 2021 7335.9 7,354.70 7,365.70 7,322.90 643.69M -0.26% 2021-07-15
## 168 Jul 14, 2021 7354.7 7,332.10 7,368.50 7,326.50 561.31M 0.31% 2021-07-14
## 169 Jul 13, 2021 7332.1 7,333.50 7,382.20 7,332.10 616.75M -0.02% 2021-07-13
## 170 Jul 12, 2021 7333.5 7,273.30 7,353.40 7,273.30 471.35M 0.83% 2021-07-12
## 171 Jul 09, 2021 7273.3 7,341.40 7,341.40 7,226.00 621.91M -0.93% 2021-07-09
## 172 Jul 08, 2021 7341.4 7,326.90 7,381.90 7,326.90 573.96M 0.20% 2021-07-08
## 173 Jul 07, 2021 7326.9 7,261.80 7,332.40 7,255.50 706.86M 0.90% 2021-07-07
## 174 Jul 06, 2021 7261.8 7,315.00 7,346.20 7,261.80 552.71M -0.73% 2021-07-06
## 175 Jul 05, 2021 7315.0 7,308.60 7,343.70 7,306.70 548.27M 0.09% 2021-07-05
## 176 Jul 02, 2021 7308.6 7,265.60 7,312.30 7,265.60 501.02M 0.59% 2021-07-02
## 177 Jul 01, 2021 7265.6 7,313.00 7,317.20 7,265.60 573.31M -0.65% 2021-07-01
## 178 Jun 30, 2021 7313.0 7,301.20 7,370.10 7,301.20 675.23M 0.16% 2021-06-30
## 179 Jun 29, 2021 7301.2 7,308.00 7,308.00 7,241.50 514.75M -0.08% 2021-06-29
## 180 Jun 28, 2021 7307.3 7,308.00 7,310.60 7,273.70 515.55M -0.01% 2021-06-28
## 181 Jun 25, 2021 7308.0 7,275.30 7,325.60 7,275.30 589.93M 0.45% 2021-06-25
## 182 Jun 24, 2021 7275.3 7,298.50 7,303.80 7,256.30 583.50M -0.32% 2021-06-24
## 183 Jun 23, 2021 7298.5 7,342.20 7,344.00 7,292.90 587.63M -0.60% 2021-06-23
## 184 Jun 22, 2021 7342.2 7,235.30 7,365.90 7,235.30 761.07M 1.48% 2021-06-22
## 185 Jun 21, 2021 7235.3 7,368.90 7,368.90 7,216.60 607.37M -1.81% 2021-06-21
## 186 Jun 18, 2021 7368.9 7,359.00 7,403.20 7,334.90 1.23B 0.13% 2021-06-18
## 187 Jun 17, 2021 7359.0 7,386.20 7,386.20 7,341.90 772.07M -0.37% 2021-06-17
## 188 Jun 16, 2021 7386.2 7,379.50 7,406.20 7,372.40 663.03M 0.09% 2021-06-16
## 189 Jun 15, 2021 7379.5 7,312.30 7,398.60 7,312.30 613.74M 0.92% 2021-06-15
## 190 Jun 10, 2021 7302.5 7,270.20 7,314.60 7,265.60 630.69M 0.44% 2021-06-10
## 191 Jun 09, 2021 7270.2 7,292.60 7,334.90 7,270.20 567.09M -0.31% 2021-06-09
## 192 Jun 08, 2021 7292.6 7,281.90 7,315.60 7,267.60 504.73M 0.15% 2021-06-08
## 193 Jun 07, 2021 7281.9 7,295.40 7,309.40 7,269.40 471.21M -0.19% 2021-06-07
## 194 Jun 04, 2021 7295.4 7,260.10 7,300.50 7,244.30 645.70M 0.49% 2021-06-04
## 195 Jun 03, 2021 7260.1 7,217.80 7,281.80 7,217.80 592.84M 0.59% 2021-06-03
## 196 Jun 02, 2021 7217.8 7,142.60 7,218.90 7,142.60 574.83M 1.05% 2021-06-02
## 197 Jun 01, 2021 7142.6 7,161.60 7,163.00 7,117.50 414.77M -0.27% 2021-06-01
## 198 May 31, 2021 7161.6 7,179.50 7,203.30 7,157.20 499.77M -0.25% 2021-05-31
## 199 May 28, 2021 7179.5 7,094.90 7,186.80 7,094.90 606.89M 1.19% 2021-05-28
## 200 May 27, 2021 7094.9 7,092.50 7,118.90 7,082.40 1.40B 0.03% 2021-05-27
## 201 May 26, 2021 7092.5 7,115.20 7,136.40 7,090.10 517.99M -0.32% 2021-05-26
## 202 May 25, 2021 7115.2 7,045.90 7,115.20 7,045.90 483.42M 0.98% 2021-05-25
## 203 May 20, 2021 7019.6 6,931.70 7,025.40 6,919.40 664.42M 1.27% 2021-05-20
## 204 May 19, 2021 6931.7 7,066.00 7,066.00 6,917.30 702.25M -1.90% 2021-05-19
## 205 May 18, 2021 7066.0 7,023.60 7,083.60 7,023.60 524.94M 0.60% 2021-05-18
## 206 May 17, 2021 7023.6 7,014.20 7,065.70 7,014.20 453.25M 0.13% 2021-05-17
## 207 May 14, 2021 7014.2 6,982.70 7,055.70 6,982.70 520.96M 0.45% 2021-05-14
## 208 May 13, 2021 6982.7 7,044.90 7,044.90 6,966.50 617.00M -0.88% 2021-05-13
## 209 May 12, 2021 7044.9 7,097.00 7,097.00 7,006.60 644.65M -0.73% 2021-05-12
## 210 May 11, 2021 7097.0 7,172.80 7,172.80 7,078.00 558.92M -1.06% 2021-05-11
## 211 May 10, 2021 7172.8 7,080.80 7,172.80 7,076.10 588.42M 1.30% 2021-05-10
## 212 May 07, 2021 7080.8 7,061.70 7,101.20 7,054.10 532.25M 0.27% 2021-05-07
## 213 May 06, 2021 7061.7 7,095.80 7,112.50 7,042.70 625.15M -0.48% 2021-05-06
## 214 May 05, 2021 7095.8 7,067.90 7,122.00 7,054.00 510.16M 0.39% 2021-05-05
## 215 May 04, 2021 7067.9 7,028.80 7,069.50 7,028.80 484.50M 0.56% 2021-05-04
## 216 May 03, 2021 7028.8 7,025.80 7,068.40 7,022.00 468.23M 0.04% 2021-05-03
## 217 Apr 30, 2021 7025.8 7,082.30 7,082.30 7,012.80 615.51M -0.80% 2021-04-30
## 218 Apr 29, 2021 7082.3 7,064.70 7,096.90 7,064.70 634.86M 0.25% 2021-04-29
## 219 Apr 28, 2021 7064.7 7,033.80 7,077.50 7,028.60 620.72M 0.44% 2021-04-28
## 220 Apr 27, 2021 7033.8 7,045.60 7,053.30 7,005.90 596.35M -0.17% 2021-04-27
## 221 Apr 26, 2021 7045.6 7,060.70 7,075.20 7,045.60 427.51M -0.21% 2021-04-26
## 222 Apr 23, 2021 7060.7 7,055.40 7,060.70 7,036.80 485.41M 0.08% 2021-04-23
## 223 Apr 22, 2021 7055.4 6,997.50 7,055.40 6,993.70 663.98M 0.83% 2021-04-22
## 224 Apr 21, 2021 6997.5 7,017.80 7,017.80 6,905.40 689.08M -0.29% 2021-04-21
## 225 Apr 20, 2021 7017.8 7,065.60 7,067.20 7,008.80 554.64M -0.68% 2021-04-20
## 226 Apr 19, 2021 7065.6 7,063.50 7,094.80 7,063.50 473.52M 0.03% 2021-04-19
## 227 Apr 16, 2021 7063.5 7,058.60 7,066.40 7,030.10 627.01M 0.07% 2021-04-16
## 228 Apr 15, 2021 7058.6 7,023.10 7,071.50 6,988.60 686.79M 0.51% 2021-04-15
## 229 Apr 14, 2021 7023.1 6,976.90 7,027.40 6,976.90 576.45M 0.66% 2021-04-14
## 230 Apr 13, 2021 6976.9 6,974.00 6,998.00 6,962.90 507.31M 0.04% 2021-04-13
## 231 Apr 12, 2021 6974.0 6,996.70 6,996.70 6,957.40 405.10M -0.30% 2021-04-12
## 232 Apr 09, 2021 6995.2 6,998.80 6,998.80 6,965.50 425.91M -0.05% 2021-04-09
## 233 Apr 08, 2021 6998.8 6,928.00 7,012.40 6,928.00 544.99M 1.02% 2021-04-08
## 234 Apr 07, 2021 6928.0 6,885.90 6,933.90 6,885.90 612.67M 0.61% 2021-04-07
## 235 Apr 06, 2021 6885.9 6,828.70 6,915.70 6,828.70 517.51M 0.84% 2021-04-06
## 236 Apr 01, 2021 6828.7 6,790.70 6,830.40 6,783.30 473.19M 0.56% 2021-04-01
## 237 Mar 31, 2021 6790.7 6,738.40 6,862.60 6,738.40 717.60M 0.78% 2021-03-31
## 238 Mar 30, 2021 6738.4 6,799.50 6,836.30 6,738.40 513.84M -0.90% 2021-03-30
## 239 Mar 29, 2021 6799.5 6,824.20 6,860.60 6,794.10 502.07M -0.36% 2021-03-29
## 240 Mar 26, 2021 6824.2 6,790.60 6,835.00 6,790.60 571.46M 0.49% 2021-03-26
## 241 Mar 25, 2021 6790.6 6,778.80 6,806.00 6,769.90 558.91M 0.17% 2021-03-25
## 242 Mar 24, 2021 6778.8 6,745.40 6,799.10 6,735.60 503.00M 0.50% 2021-03-24
## 243 Mar 23, 2021 6745.4 6,752.50 6,786.20 6,741.30 518.77M -0.11% 2021-03-23
## 244 Mar 22, 2021 6752.5 6,708.20 6,763.40 6,688.20 472.04M 0.66% 2021-03-22
## 245 Mar 19, 2021 6708.2 6,745.90 6,745.90 6,673.70 1.25B -0.56% 2021-03-19
## 246 Mar 18, 2021 6745.9 6,795.20 6,806.20 6,744.50 715.60M -0.73% 2021-03-18
## 247 Mar 17, 2021 6795.2 6,827.10 6,827.10 6,761.40 603.82M -0.47% 2021-03-17
## 248 Mar 16, 2021 6827.1 6,773.00 6,858.90 6,767.80 587.15M 0.80% 2021-03-16
## 249 Mar 15, 2021 6773.0 6,766.80 6,793.50 6,727.50 484.55M 0.09% 2021-03-15
## 250 Mar 12, 2021 6766.8 6,713.90 6,783.00 6,713.90 468.30M 0.79% 2021-03-12
## 251 Mar 11, 2021 6713.9 6,714.10 6,756.70 6,648.60 665.89M -0.00% 2021-03-11
## 252 Mar 10, 2021 6714.1 6,771.20 6,806.50 6,714.10 619.77M -0.84% 2021-03-10
## 253 Mar 09, 2021 6771.2 6,739.60 6,810.00 6,739.60 733.62M 0.47% 2021-03-09
## 254 Mar 08, 2021 6739.6 6,710.80 6,835.60 6,710.80 609.64M 0.43% 2021-03-08
## 255 Mar 05, 2021 6710.8 6,760.70 6,760.70 6,660.50 702.40M -0.74% 2021-03-05
## 256 Mar 04, 2021 6760.7 6,818.00 6,818.00 6,709.00 737.49M -0.84% 2021-03-04
## 257 Mar 03, 2021 6818.0 6,762.30 6,818.80 6,762.30 591.22M 0.82% 2021-03-03
## 258 Mar 02, 2021 6762.3 6,789.60 6,860.70 6,762.30 739.31M -0.40% 2021-03-02
## 259 Mar 01, 2021 6789.6 6,673.30 6,790.10 6,672.20 734.33M 1.74% 2021-03-01
## 260 Feb 26, 2021 6673.3 6,834.00 6,834.00 6,658.90 1.03B -2.35% 2021-02-26
## 261 Feb 25, 2021 6834.0 6,777.80 6,857.30 6,777.80 806.72M 0.83% 2021-02-25
## 262 Feb 24, 2021 6777.8 6,839.20 6,839.20 6,762.60 677.31M -0.90% 2021-02-24
## 263 Feb 23, 2021 6839.2 6,780.90 6,839.20 6,765.30 806.33M 0.86% 2021-02-23
## 264 Feb 22, 2021 6780.9 6,793.80 6,824.80 6,774.30 653.24M -0.19% 2021-02-22
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## 166 145 1 Australia 162759 140981 25788217
## 167 113 0 Australia 171692 113068 25788217
## 168 84 0 Australia 170025 126082 25788217
## 169 106 0 Australia 168921 102796 25788217
## 170 102 1 Australia 146118 130133 25788217
## 171 64 0 Australia 144830 98104 25788217
## 172 48 0 Australia 156750 99235 25788217
## 173 42 0 Australia 149630 82037 25788217
## 174 30 0 Australia 159464 93563 25788217
## 175 27 0 Australia 135044 116934 25788217
## 176 50 0 Australia 154817 162784 25788217
## 177 41 0 Australia 185318 155772 25788217
## 178 41 0 Australia 96951 150302 25788217
## 179 42 0 Australia 144885 126945 25788217
## 180 32 0 Australia 126034 100163 25788217
## 181 33 0 Australia 119973 91915 25788217
## 182 20 0 Australia 138434 93522 25788217
## 183 24 0 Australia 138922 96664 25788217
## 184 14 0 Australia 140892 59835 25788217
## 185 11 0 Australia 128643 60188 25788217
## 186 13 0 Australia 130810 75644 25788217
## 187 8 0 Australia 141336 70468 25788217
## 188 15 0 Australia 136010 47437 25788217
## 189 13 0 Australia 152075 42936 25788217
## 190 3 0 Australia 153338 67478 25788217
## 191 15 0 Australia 142808 73284 25788217
## 192 5 0 Australia 140885 54037 25788217
## 193 14 0 Australia 127564 89000 25788217
## 194 16 0 Australia 141245 89027 25788217
## 195 4 0 Australia 143659 96003 25788217
## 196 13 0 Australia 141259 93405 25788217
## 197 6 0 Australia 138705 68241 25788217
## 198 13 0 Australia 119139 76663 25788217
## 199 9 0 Australia 121610 95647 25788217
## 200 11 0 Australia 124871 81048 25788217
## 201 17 0 Australia 111388 61494 25788217
## 202 17 0 Australia 104658 35564 25788217
## 203 4 0 Australia 101146 55335 25788217
## 204 3 0 Australia 92874 58524 25788217
## 205 9 0 Australia 95530 39354 25788217
## 206 5 0 Australia 83187 36132 25788217
## 207 7 0 Australia 76153 54093 25788217
## 208 2 0 Australia 85874 55232 25788217
## 209 9 0 Australia 82284 61475 25788217
## 210 8 0 Australia 76379 35191 25788217
## 211 7 0 Australia 72886 36799 25788217
## 212 13 0 Australia 73194 48969 25788217
## 213 9 0 Australia 81002 49746 25788217
## 214 19 0 Australia 77215 53237 25788217
## 215 15 0 Australia 79345 30733 25788217
## 216 12 0 Australia 56354 28900 25788217
## 217 13 0 Australia 55300 44266 25788217
## 218 25 0 Australia 67259 50422 25788217
## 219 23 0 Australia 82741 59461 25788217
## 220 33 0 Australia 60207 42490 25788217
## 221 37 0 Australia 31852 40627 25788217
## 222 15 0 Australia 58446 33457 25788217
## 223 44 0 Australia 69903 37186 25788217
## 224 18 0 Australia 67591 37749 25788217
## 225 20 0 Australia 64821 27878 25788217
## 226 23 0 Australia 66730 28648 25788217
## 227 15 0 Australia 53981 39152 25788217
## 228 15 0 Australia 61272 44406 25788217
## 229 19 0 Australia 63633 45793 25788217
## 230 21 0 Australia 60991 28512 25788217
## 231 10 1 Australia 56379 32317 25788217
## 232 6 0 Australia 61355 50988 25788217
## 233 5 0 Australia 81297 44549 25788217
## 234 6 0 Australia 75880 32561 25788217
## 235 14 0 Australia 65351 28581 25788217
## 236 11 0 Australia 79283 85667 25788217
## 237 18 0 Australia 73979 84676 25788217
## 238 8 0 Australia 72826 44989 25788217
## 239 20 0 Australia 55764 45086 25788217
## 240 13 0 Australia 46310 38818 25788217
## 241 9 0 Australia 51745 39005 25788217
## 242 9 0 Australia 49908 47726 25788217
## 243 10 0 Australia 46000 29572 25788217
## 244 5 0 Australia 30542 30548 25788217
## 245 9 0 Australia 13077 44082 25788217
## 246 17 0 Australia 14697 50496 25788217
## 247 12 0 Australia 22500 54260 25788217
## 248 17 0 Australia 21120 33843 25788217
## 249 7 0 Australia 17656 32312 25788217
## 250 10 0 Australia 24185 45015 25788217
## 251 12 0 Australia 10109 51011 25788217
## 252 16 0 Australia 16672 47309 25788217
## 253 13 0 Australia 13420 27603 25788217
## 254 15 0 Australia 8539 35146 25788217
## 255 8 0 Australia 5073 47635 25788217
## 256 14 0 Australia 10858 49049 25788217
## 257 11 0 Australia 9939 52921 25788217
## 258 10 0 Australia 9163 39974 25788217
## 259 8 0 Australia 7277 24226 25788217
## 260 8 0 Australia 6490 47899 25788217
## 261 10 0 Australia 6881 49940 25788217
## 262 8 0 Australia 9715 56776 25788217
## 263 2 0 Australia 4125 43521 25788217
## 264 7 0 Australia 2769 36501 25788217
## population_density
## 1 3.202
## 2 3.202
## 3 3.202
## 4 3.202
## 5 3.202
## 6 3.202
## 7 3.202
## 8 3.202
## 9 3.202
## 10 3.202
## 11 3.202
## 12 3.202
## 13 3.202
## 14 3.202
## 15 3.202
## 16 3.202
## 17 3.202
## 18 3.202
## 19 3.202
## 20 3.202
## 21 3.202
## 22 3.202
## 23 3.202
## 24 3.202
## 25 3.202
## 26 3.202
## 27 3.202
## 28 3.202
## 29 3.202
## 30 3.202
## 31 3.202
## 32 3.202
## 33 3.202
## 34 3.202
## 35 3.202
## 36 3.202
## 37 3.202
## 38 3.202
## 39 3.202
## 40 3.202
## 41 3.202
## 42 3.202
## 43 3.202
## 44 3.202
## 45 3.202
## 46 3.202
## 47 3.202
## 48 3.202
## 49 3.202
## 50 3.202
## 51 3.202
## 52 3.202
## 53 3.202
## 54 3.202
## 55 3.202
## 56 3.202
## 57 3.202
## 58 3.202
## 59 3.202
## 60 3.202
## 61 3.202
## 62 3.202
## 63 3.202
## 64 3.202
## 65 3.202
## 66 3.202
## 67 3.202
## 68 3.202
## 69 3.202
## 70 3.202
## 71 3.202
## 72 3.202
## 73 3.202
## 74 3.202
## 75 3.202
## 76 3.202
## 77 3.202
## 78 3.202
## 79 3.202
## 80 3.202
## 81 3.202
## 82 3.202
## 83 3.202
## 84 3.202
## 85 3.202
## 86 3.202
## 87 3.202
## 88 3.202
## 89 3.202
## 90 3.202
## 91 3.202
## 92 3.202
## 93 3.202
## 94 3.202
## 95 3.202
## 96 3.202
## 97 3.202
## 98 3.202
## 99 3.202
## 100 3.202
## 101 3.202
## 102 3.202
## 103 3.202
## 104 3.202
## 105 3.202
## 106 3.202
## 107 3.202
## 108 3.202
## 109 3.202
## 110 3.202
## 111 3.202
## 112 3.202
## 113 3.202
## 114 3.202
## 115 3.202
## 116 3.202
## 117 3.202
## 118 3.202
## 119 3.202
## 120 3.202
## 121 3.202
## 122 3.202
## 123 3.202
## 124 3.202
## 125 3.202
## 126 3.202
## 127 3.202
## 128 3.202
## 129 3.202
## 130 3.202
## 131 3.202
## 132 3.202
## 133 3.202
## 134 3.202
## 135 3.202
## 136 3.202
## 137 3.202
## 138 3.202
## 139 3.202
## 140 3.202
## 141 3.202
## 142 3.202
## 143 3.202
## 144 3.202
## 145 3.202
## 146 3.202
## 147 3.202
## 148 3.202
## 149 3.202
## 150 3.202
## 151 3.202
## 152 3.202
## 153 3.202
## 154 3.202
## 155 3.202
## 156 3.202
## 157 3.202
## 158 3.202
## 159 3.202
## 160 3.202
## 161 3.202
## 162 3.202
## 163 3.202
## 164 3.202
## 165 3.202
## 166 3.202
## 167 3.202
## 168 3.202
## 169 3.202
## 170 3.202
## 171 3.202
## 172 3.202
## 173 3.202
## 174 3.202
## 175 3.202
## 176 3.202
## 177 3.202
## 178 3.202
## 179 3.202
## 180 3.202
## 181 3.202
## 182 3.202
## 183 3.202
## 184 3.202
## 185 3.202
## 186 3.202
## 187 3.202
## 188 3.202
## 189 3.202
## 190 3.202
## 191 3.202
## 192 3.202
## 193 3.202
## 194 3.202
## 195 3.202
## 196 3.202
## 197 3.202
## 198 3.202
## 199 3.202
## 200 3.202
## 201 3.202
## 202 3.202
## 203 3.202
## 204 3.202
## 205 3.202
## 206 3.202
## 207 3.202
## 208 3.202
## 209 3.202
## 210 3.202
## 211 3.202
## 212 3.202
## 213 3.202
## 214 3.202
## 215 3.202
## 216 3.202
## 217 3.202
## 218 3.202
## 219 3.202
## 220 3.202
## 221 3.202
## 222 3.202
## 223 3.202
## 224 3.202
## 225 3.202
## 226 3.202
## 227 3.202
## 228 3.202
## 229 3.202
## 230 3.202
## 231 3.202
## 232 3.202
## 233 3.202
## 234 3.202
## 235 3.202
## 236 3.202
## 237 3.202
## 238 3.202
## 239 3.202
## 240 3.202
## 241 3.202
## 242 3.202
## 243 3.202
## 244 3.202
## 245 3.202
## 246 3.202
## 247 3.202
## 248 3.202
## 249 3.202
## 250 3.202
## 251 3.202
## 252 3.202
## 253 3.202
## 254 3.202
## 255 3.202
## 256 3.202
## 257 3.202
## 258 3.202
## 259 3.202
## 260 3.202
## 261 3.202
## 262 3.202
## 263 3.202
## 264 3.202Initial analysis for new_cases_smoothed
covid_temp <- covid_clean
covid_temp$month <- strftime(covid_temp$date, "%m")
covid_temp$year <- strftime(covid_temp$date, "%Y")
covid_temp_new_case_aggregate <- aggregate(new_cases_smoothed~month+year,
covid_temp,
FUN = mean)
covid_temp_new_case_aggregate$month_year <- paste(covid_temp_new_case_aggregate$month, covid_temp_new_case_aggregate$year)ggplot(covid_temp_new_case_aggregate, aes(x= new_cases_smoothed, y= month_year)) +
geom_bar(stat = 'identity') + ggtitle("Average New Cases (Smoothed) per Month") +
ylab("month year") + xlab("average new cases")- Analysis: From the above bar plot, we set different months of 2021 and 2022 as y-axis and the average smoothed new cases for each month as x-axis. We can easily know that smoothed new cases in most months are less than 30,000 except in Jan, 2022, which is nearly 50,000. So the data of new_cases_smoothed is evenly distributed and has a small standard deviation.
Initial analysis for new_people_vaccinated_smoothed
covid_temp = covid_clean %>% select(date,new_people_vaccinated_smoothed)
covid_temp$date <- as.Date(covid_temp$date, format = "%Y-%m-%d")
covid_temp_new_vaccinated_aggregate = covid_temp %>% mutate(month_year = as.character(format(date, "%m-%Y"))) %>%
group_by(month_year) %>%
summarise(date=date[1], number = mean(new_people_vaccinated_smoothed))
covid_temp_new_vaccinated_aggregate ggplot(covid_temp_new_vaccinated_aggregate, aes(x = number, y= month_year)) +
geom_bar(stat = 'identity') + ggtitle("Average New People Vaccinated (Smoothed) per Month") +
ylab("month year") + xlab("average new people vaccinated")- Analysis: Most number show above are larger than 100000. Many people get vaccinated in July, August and September in 2021, and less people get vaccinated before and after that period which is reasonable since it takes time to invent and promote new vaccines.
Q2 a
Price ~ new_cases
#glimpse(df_aus)
df_aus_subset = df_aus %>% select(Price, new_cases)
view(df_aus_subset)
M0 = lm(Price ~ new_cases, data = df_aus_subset) # Null model
summary(M0)##
## Call:
## lm(formula = Price ~ new_cases, data = df_aus_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -549.62 -157.03 60.97 163.28 405.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.223e+03 1.525e+01 473.504 <2e-16 ***
## new_cases 1.166e-03 5.762e-04 2.024 0.044 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 226.3 on 262 degrees of freedom
## Multiple R-squared: 0.0154, Adjusted R-squared: 0.01164
## F-statistic: 4.097 on 1 and 262 DF, p-value: 0.04396- Analysis(Paul and Christin): We build a linear model here to explore relationship between stock prices and new cases of covid-19. The dependent variable here is stock prices while independent variable is new cases. Although the r-squared value is quite low and Residual standard error is a bit high, which indicates our model is not good, the p-value for new cases is 0.044( < 0.05), so it is a significantly influence Price. The final model we get is \(\text{Price} = 1.166e^{-03}\times \text{new_cases} + 7.223e^{03}\).
Scatter Plot for price and new_cases
df_aus = df_aus[order(as.Date(df_aus$date, format="%d/%m/%Y")),]
df_aus$logPrice = log(df_aus$Price)
colnames(df_aus)[7] = 'Change'
#write.csv(df_aus,"df_aus.csv")y <- df_aus$Price
x <- df_aus$new_cases
plot(x, y, main = "Price ~ New Cases",
ylab = "Price", xlab = "new_cases",
pch = 19, frame = FALSE)
# Add regression line
plot(x, y, main = "Price ~ New Cases",
ylab = "Price", xlab = "new_cases",
pch = 19, frame = FALSE)
abline(lm(y ~ x, data = df_aus), col = "blue")
Price ~ new_vaccinations
# df_aus_subset = df_aus %>% select(Price, new_deaths) %>% filter(new_deaths != 0)New deaths and prices
df_aus_subset = df_aus %>% select(Price, new_deaths, new_vaccinations, new_cases)
view(df_aus_subset)
M1 = lm(Price ~ poly(new_vaccinations, degree=2), data=df_aus_subset)
summary(M1)##
## Call:
## lm(formula = Price ~ poly(new_vaccinations, degree = 2), data = df_aus_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -555.39 -72.58 -0.10 77.58 697.87
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7235.494 8.446 856.70 <2e-16 ***
## poly(new_vaccinations, degree = 2)1 2467.685 137.228 17.98 <2e-16 ***
## poly(new_vaccinations, degree = 2)2 -1620.816 137.228 -11.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 137.2 on 261 degrees of freedom
## Multiple R-squared: 0.6394, Adjusted R-squared: 0.6367
## F-statistic: 231.4 on 2 and 261 DF, p-value: < 2.2e-16ggplot(data = df_aus_subset, mapping = aes(x = Price, y = new_deaths)) + geom_point() + ggtitle("Prices of stocks and against new death") + ylab("New deaths") + xlab("Price")
Price ~ new_vaccinations
ggplot(data = df_aus, mapping = aes(x = new_vaccinations, y = Price)) + geom_point() + ggtitle("Prices of stocks against new vaccinations") + xlab("New vaccinations") + ylab("Price")
Price ~ New tests
df_aus_subset = df_aus %>% select(Price, new_tests)
view(df_aus_subset)
M2 = lm(Price ~ poly(new_tests, degree=2), data=df_aus_subset)
summary(M2)##
## Call:
## lm(formula = Price ~ poly(new_tests, degree = 2), data = df_aus_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -469.40 -88.38 12.24 86.71 401.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7235.494 8.392 862.236 < 2e-16 ***
## poly(new_tests, degree = 2)1 2767.837 136.347 20.300 < 2e-16 ***
## poly(new_tests, degree = 2)2 -1057.592 136.347 -7.757 1.97e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 136.3 on 261 degrees of freedom
## Multiple R-squared: 0.6441, Adjusted R-squared: 0.6413
## F-statistic: 236.1 on 2 and 261 DF, p-value: < 2.2e-16ggplot(data = df_aus, mapping = aes(x = new_tests, y = Price)) + geom_point() + ggtitle("Prices of stocks against new tests") + xlab("New tests") + ylab("Price")
ggplot(data = df_aus, mapping = aes(x = Price, y = population)) + geom_point() + ggtitle("Prices of stocks against population") + ylab("New tests") + xlab("Price")
ggplot(data = df_aus, mapping = aes(x = Price, y = population_density)) + geom_point() + ggtitle("Prices of stocks against population density") + ylab("New tests") + xlab("Price")
ggplot(data = df_aus, mapping = aes(x = date, y = population_density)) + geom_line() + ggtitle("Prices of stocks against population density") + ylab("New tests") + xlab("Price") # bad
summary(df_aus$Price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6673 7067 7296 7235 7400 7629df_aus$Price_mul30 = df_aus$Price*30
p1 = ggplot(data = df_aus) + geom_line(aes(x=date, y = new_cases), color = "red") + geom_line(aes(x=date, y = new_vaccinations), color = "light green") + geom_line(aes(x=date, y = new_tests), color = "light blue") +
geom_line(aes(x=date, y = Price_mul30), color = "blue") + theme_bw()
df_aus$Price_mul30 = df_aus$Price*30
p1 = ggplot(data = df_aus) + geom_line(aes(x=date, y = new_cases), color = "red") + geom_line(aes(x=date, y = new_vaccinations), color = "light green") + geom_line(aes(x=date, y = new_tests), color = "light blue")+ theme_bw()
ggplotly(p1)autoplot(M0, which = 1:2)
autoplot(M1, which = 1:2)
autoplot(M2, which = 1:2)M = lm(logPrice ~ polym(new_tests, new_vaccinations, degree=2, raw=TRUE), data=df_aus)
summary(M)##
## Call:
## lm(formula = logPrice ~ polym(new_tests, new_vaccinations, degree = 2,
## raw = TRUE), data = df_aus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068522 -0.008047 0.001027 0.009477 0.044903
##
## Coefficients:
## Estimate
## (Intercept) 8.804e+00
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 3.844e-07
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 -3.683e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 5.650e-07
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 -3.685e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 -1.171e-12
## Std. Error
## (Intercept) 3.484e-03
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 5.923e-08
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 2.030e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 4.766e-08
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 2.215e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 1.302e-13
## t value Pr(>|t|)
## (Intercept) 2526.972 < 2e-16
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 6.491 4.34e-10
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 -1.814 0.0708
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 11.856 < 2e-16
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 -1.664 0.0974
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 -8.993 < 2e-16
##
## (Intercept) ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 .
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 .
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01542 on 258 degrees of freedom
## Multiple R-squared: 0.7704, Adjusted R-squared: 0.7659
## F-statistic: 173.1 on 5 and 258 DF, p-value: < 2.2e-16ggplot(data = df_aus) + geom_point(aes(x = new_tests, y = logPrice), color = "light blue") +
geom_point(aes(x = new_vaccinations, y = logPrice), color = "light green") +
ggtitle("Prices of stocks against new tests and new_vaccinations") + ylab("Price") +theme_classic()
- The behaviour of a dependent variable is explained by a linear, or curvilinear, additive relationship between the dependent variable and a set of k independent variables (xi, i=1 to k).
- The relationship between the dependent variable and any independent variable is linear or curvilinear.
- The independent variables do no depend on each other too.
- The errors are independent, normally distributed with mean zero and a constant variance.